Another alternative is a genetic algorithm (GA), used to derive a closed form expression of the received PL [1]}. Other well known regression algorithms, such as the \(k\) -nearest neighbors, are also popular for radio propagation modeling [2]}, [3]}. Recently, ensemble methods have been used with promising results [4]}. Their function is based on the simple notion that a combination of learners can be more powerful than each one separately. Random forests (RF) [5]}, which operate by constructing a variety of decision trees, is such an ensemble method, used for radio propagation modeling [4]}, [2]}, [8]}, [3]}. Another form of ensemble learning is boosting. One of its more popular implementations, the Adaboost algorithm [5]}, has also been applied for propagation modeling [4]}, [2]}, [8]}, [3]}. Any one of the models previously mentioned, such as the SVR or the \(k\) -NN algorithms, can be used as a learner for the ensemble method. Since ensemble methods require the participation of many learners for the generation of the final output, a weighting scheme between them has to be chosen. Finally, it should be noted that training ensemble methods may take considerable time, compared to separately training the base learner.
